Rapid Peptide Cyclization Inspired by the Modular Logic of Nonribosomal Peptide Synthetases.

Nonribosomal cyclic peptides (NRcPs) are structurally complex natural products and a vital pool of therapeutics, particularly antibiotics. Their structural diversity arises from the ability of the multidomain enzyme assembly lines, nonribosomal peptide synthetases (NRPSs), to utilize bespoke nonproteinogenic amino acids, modify the linear peptide during elongation, and catalyze an array of cyclization modes, e.g., head to tail, side chain to tail. The study and drug development of NRcPs are often limited by a lack of easy synthetic access to NRcPs and their analogues, with selective macrolactamization being a major bottleneck. Herein, we report a generally applicable chemical macrocyclization method of unprecedented speed and selectivity. Inspired by biosynthetic cyclization, it combines the deprotected linear biosynthetic precursor peptide sequence with a highly reactive C-terminus to produce NRcPs and analogues in minutes. The method was applied to several NRcPs of varying sequences, ring sizes, and cyclization modes including rufomycin, colistin, and gramicidin S with comparable success. We thus demonstrate that the linear order of modules in NRPS enzymes that determines peptide sequence encodes the key structural information to produce peptides conformationally biased toward macrocyclization. To fully exploit this conformational bias synthetically, a highly reactive C-terminal acyl azide is also required, alongside carefully balanced pH and solvent conditions. This allows for consistent, facile cyclization of exceptional speed, selectivity, and atom efficiency. This exciting macrolactamization method represents a new enabling technology for the biosynthetic study of NRcPs and their development as therapeutics.

Therapeutic Peptides Are Preferentially Solubilized in Specific Microenvironments within PEG-PLGA Polymer Nanoparticles.

Polymeric nanoparticles are a highly promising drug delivery formulation. However, a lack of understanding of the molecular mechanisms that underlie their drug solubilization and controlled release capabilities has hindered the efficient clinical translation of such technologies. Polyethylene glycol-poly(lactic-co-glycolic) acid (PEG-PLGA) nanoparticles have been widely studied as cancer drug delivery vehicles. In this letter, we use unbiased coarse-grained molecular dynamics simulations to model the self-assembly of a PEG-PLGA nanoparticle and its solubulization of the anticancer peptide, EEK, with good agreement with previously reported experimental structural data. We applied unsupervised machine learning techniques to quantify the conformations that polymers adopt at various locations within the nanoparticle. We find that the local microenvironments formed by the various polymer conformations promote preferential EEK solubilization within specific regions of the NP. This demonstrates that these microenvironments are key in controlling drug storage locations within nanoparticles, supporting the rational design of nanoparticles for therapeutic applications.

Modeling Substrate Entry into the P-Glycoprotein Efflux Pump at the Blood-Brain Barrier.

We report molecular dynamics simulations of rhodamine entry into the central binding cavity of P-gp in the inward open conformation. Rhodamine can enter the inner volume via passive transport across the luminal membrane or lateral diffusion in the lipid bilayer. Entry into the inner volume is determined by the aperture angle at the apex of the protein, with a critical angle of 27° for rhodamine. The central binding cavity has an aqueous phase with a few lipids, which significantly reduces substrate diffusion. Within the central binding cavity, we identified regions with relatively weak binding, suggesting that the combination of reduced mobility and weak substrate binding confines rhodamine to enable the completion of the efflux cycle. Tariquidar, a P-gp inhibitor, aggregates at the lower arms of the P-gp, suggesting that inhibition involves steric hindrance of entry into the inner volume and/or steric hindrance of access of ATP to the nucleotide-binding domains.

Masking the transmembrane region of the amyloid ß precursor protein as a safe means to lower amyloid ß production.

Introduction: Reducing brain levels of both soluble and insoluble forms of amyloid beta (Aß) remains the primary goal of most therapies that target Alzheimer's disease (AD). However, no treatment has so far resulted in patient benefit, and clinical trials of the most promising drug candidates have generally failed due to significant adverse effects. This highlights the need for safer and more selective ways to target and modulate Aß biogenesis. Methods: Peptide technology has advanced to allow reliable synthesis, purification, and delivery of once-challenging hydrophobic sequences. This is opening up new routes to target membrane processes associated with disease. Here we deploy a combination of atomic detail molecular dynamics (MD) simulations, living-cell Förster resonance energy transfer (FRET), and in vitro assays to elucidate the atomic-detail dynamics, molecular mechanisms, and cellular activity and selectivity of a membrane-active peptide that targets the Aß precursor protein (APP). Results: We demonstrate that Aß biogenesis can be downregulated selectively using an APP occlusion peptide (APPOP). APPOP inhibits Aß production in a dose-dependent manner, with a mean inhibitory concentration (IC50) of 450 nM toward exogenous APP and 50 nM toward endogenous APP in primary rat cortical neuronal cultures. APPOP does not impact the γ-secretase cleavage of Notch-1, or exhibit toxicity toward cultured primary rat neurons, suggesting that it selectively shields APP from proteolysis. Discussion: Drugs targeting AD need to be given early and for very long periods to prevent the onset of clinical symptoms. This necessitates being able to target Aß production precisely and without affecting the activity of key cellular enzymes such as γ-secretase for other substrates. Peptides offer a powerful way for targeting key pathways precisely, thereby reducing the risk of adverse effects. Here we show that protecting APP from proteolytic processing offers a promising route to safely and specifically lower Aß burden. In particular, we show that the amyloid pathway can be targeted directly and specificically. This reduces the risk of off-target effects and paves the way for a safe prophylactic treatment.

A least-squares-fitting procedure for an efficient preclinical ranking of passive transport across the blood-brain barrier endothelium.

The treatment of various disorders of the central nervous system (CNS) is often impeded by the limited brain exposure of drugs, which is regulated by the human blood-brain barrier (BBB). The screening of lead compounds for CNS penetration is challenging due to the biochemical complexity of the BBB, while experimental determination of permeability is not feasible for all types of compounds. Here we present a novel method for rapid preclinical screening of libraries of compounds by utilizing advancements in computing hardware, with its foundation in transition-based counting of the flux. This method has been experimentally validated for in vitro permeabilities and provides atomic-level insights into transport mechanisms. Our approach only requires a single high-temperature simulation to rank a compound relative to a library, with a typical simulation time converging within 24 to 72 h. The method offers unbiased thermodynamic and kinetic information to interpret the passive transport of small-molecule drugs across the BBB.

Development of membrane-active peptide therapeutics in oncology.

Membrane-active peptides play an essential role in many living organisms and their immune systems and counter many infectious diseases. Many have dual or multiple mechanisms and can synergize with other molecules, like peptides, proteins, and small molecules. Although membrane-active peptides have been intensively studied in the past decades and more than 3500 sequences have been identified, only a few received approvals from the US Food and Drug Administration. In this review, we investigated all the peptide therapeutics that have entered the market or were subjected to preclinical and clinical studies to understand how they succeeded. With technological advancement (e.g., chemical modifications and pharmaceutical formulations) and a better understanding of the mechanism of action and the potential targets, we found at least five membrane-active peptide drugs that have entered preclinical/clinical phases and show promising results for cancer treatment. We summarized our findings in this review and provided insights into membrane-active anticancer peptide therapeutics.

Integrated Design of a Membrane-Lytic Peptide-Based Intravenous Nanotherapeutic Suppresses Triple-Negative Breast Cancer.

Membrane-lytic peptides offer broad synthetic flexibilities and design potential to the arsenal of anticancer therapeutics, which can be limited by cytotoxicity to noncancerous cells and induction of drug resistance via stress-induced mutagenesis. Despite continued research efforts on membrane-perforating peptides for antimicrobial applications, success in anticancer peptide therapeutics remains elusive given the muted distinction between cancerous and normal cell membranes and the challenge of peptide degradation and neutralization upon intravenous delivery. Using triple-negative breast cancer as a model, the authors report the development of a new class of anticancer peptides. Through function-conserving mutations, the authors achieved cancer cell selective membrane perforation, with leads exhibiting a 200-fold selectivity over non-cancerogenic cells and superior cytotoxicity over doxorubicin against breast cancer tumorspheres. Upon continuous exposure to the anticancer peptides at growth-arresting concentrations, cancer cells do not exhibit resistance phenotype, frequently observed under chemotherapeutic treatment. The authors further demonstrate efficient encapsulation of the anticancer peptides in 20 nm polymeric nanocarriers, which possess high tolerability and lead to effective tumor growth inhibition in a mouse model of MDA-MB-231 triple-negative breast cancer. This work demonstrates a multidisciplinary approach for enabling translationally relevant membrane-lytic peptides in oncology, opening up a vast chemical repertoire to the arms race against cancer.

Predicting Membrane-Active Peptide Dynamics in Fluidic Lipid Membranes.

Understanding the interactions between peptides and lipid membranes could not only accelerate the development of antimicrobial peptides as treatments for infections but also be applied to finding targeted therapies for cancer and other diseases. However, designing biophysical experiments to study molecular interactions between flexible peptides and fluidic lipid membranes has been an ongoing challenge. Recently, with hardware advances, algorithm improvements, and more accurate parameterizations (i.e., force fields), all-atom molecular dynamics (MD) simulations have been used as a "computational microscope" to investigate the molecular interactions and mechanisms of membrane-active peptides in cell membranes (Chen et al., Curr Opin Struct Biol 61:160-166, 2020; Ulmschneider and Ulmschneider, Acc Chem Res 51(5):1106-1116, 2018; Dror et al., Annu Rev Biophys 41:429-452, 2012). In this chapter, we describe how to utilize MD simulations to predict and study peptide dynamics and how to validate the simulations by circular dichroism, intrinsic fluorescent probe, membrane leakage assay, electrical impedance, and isothermal titration calorimetry. Experimentally validated MD simulations open a new route towards peptide design starting from sequence and structure and leading to desirable functions.

Atomistic Model of Solute Transport across the Blood-Brain Barrier.

The blood-brain barrier remains a major roadblock to the delivery of drugs to the brain. While in vitro and in vivo measurements of permeability are widely used to predict brain penetration, very little is known about the mechanisms of passive transport. Detailed insight into interactions between solutes and cell membranes could provide new insight into drug design and screening. Here, we perform unbiased atomistic MD simulations to visualize translocation of a library of 24 solutes across a lipid bilayer representative of brain microvascular endothelial cells. A temperature bias is used to achieve steady state of all solutes, including those with low permeability. Based on free-energy surface profiles, we show that the solutes can be classified into three groups that describe distinct mechanisms of transport across the bilayer. Simulations down to 310 K for solutes with fast permeability were used to justify the extrapolation of values at 310 K from higher temperatures. Comparison of permeabilities at 310 K to experimental values obtained from in vitro transwell measurements and in situ brain perfusion revealed that permeabilities obtained from simulations vary from close to the experimental values to more than 3 orders of magnitude faster. The magnitude of the difference was dependent on the group defined by free-energy surface profiles. Overall, these results show that MD simulations can provide new insight into the mechanistic details of brain penetration and provide a new approach for drug discovery.

Tuning of a Membrane-Perforating Antimicrobial Peptide to Selectively Target Membranes of Different Lipid Composition.

The use of designed antimicrobial peptides as drugs has been impeded by the absence of simple sequence-structure-function relationships and design rules. The likely cause is that many of these peptides permeabilize membranes via highly disordered, heterogeneous mechanisms, forming aggregates without well-defined tertiary or secondary structure. We suggest that the combination of high-throughput library screening with atomistic computer simulations can successfully address this challenge by tuning a previously developed general pore-forming peptide into a selective pore-former for different lipid types. A library of 2916 peptides was designed based on the LDKA template. The library peptides were synthesized and screened using a high-throughput orthogonal vesicle leakage assay. Dyes of different sizes were entrapped inside vesicles with varying lipid composition to simultaneously screen for both pore size and affinity for negatively charged and neutral lipid membranes. From this screen, nine different LDKA variants that have unique activity were selected, sequenced, synthesized, and characterized. Despite the minor sequence changes, each of these peptides has unique functional properties, forming either small or large pores and being selective for either neutral or anionic lipid bilayers. Long-scale, unbiased atomistic molecular dynamics (MD) simulations directly reveal that rather than rigid, well-defined pores, these peptides can form a large repertoire of functional dynamic and heterogeneous aggregates, strongly affected by single mutations. Predicting the propensity to aggregate and assemble in a given environment from sequence alone holds the key to functional prediction of membrane permeabilization.

Understanding and modelling the interactions of peptides with membranes: from partitioning to self-assembly.

Atomic detail simulations are starting to reveal how flexible polypeptides interact with fluid lipid bilayers. These insights are transforming our understanding of one of the fundamental processes in biology: membrane protein folding and assembly. Advanced molecular dynamics (MD) simulation techniques enable accurate prediction of protein structure, folding pathways and assembly in microsecond-timescales. Such simulations show how membrane-active peptides self-assemble in cell membranes, revealing their binding, folding, insertion, and aggregation, while at the same time providing atomic resolution details of peptide-lipid interactions. Essential to the impact of simulations are experimental approaches that enable calibration and validation of the computational models and techniques. In this review, we summarize the current development of applying unbiased atomic detail MD simulations and the relation to experimental techniques, to study peptide folding and provide our perspective of the field.

An experimentally validated approach to calculate the blood-brain barrier permeability of small molecules.

Drug development for the treatment of central nervous system (CNS) diseases is extremely challenging, in large part due to the difficulty in crossing the blood-brain barrier (BBB). Here we develop and experimentally validate a new in silico method to predict quantitatively the BBB permeability for small-molecule drugs. We show accurate prediction of solute permeabilities at physiological temperature using high-temperature unbiased atomic detail molecular dynamics simulations of spontaneous drug diffusion across BBB bilayers. These simulations provide atomic detail insights into the transport mechanisms, as well as converged kinetics and thermodynamics. The method is validated computationally against physiological temperature simulations for fast-diffusing compounds, as well as experimentally by direct determination of the compound permeabilities using a transwell assay as an in vitro BBB model. The overall agreement of the predicted values with both direct simulations at physiological temperatures and experimental data is excellent. This new tool has the potential to replace current semi-empirical in silico screening and in vitro permeability measurements in CNS drug discovery.

Simulation-Guided Rational de Novo Design of a Small Pore-Forming Antimicrobial Peptide.

In the age of failing small-molecule antibiotics, tapping the near-infinite structural and chemical repertoire of antimicrobial peptides (AMPs) offers one of the most promising routes toward developing next-generation antibacterial compounds. One of the key impediments en route is the lack of methodologies for systematic rational design and optimization of new AMPs. Here we present a new simulation-guided rational design approach and apply it to develop a potent new AMP. We show that unbiased atomic detail molecular dynamics (MD) simulations are able to predict structures formed by evolving peptide designs enabling structure-based rational fine-tuning of functional properties. Starting from a 14-residue poly leucine template we demonstrate the design of a minimalistic potent new AMP. Consisting of only four types of amino acids (LDKA), this peptide forms large pores in microbial membranes at very low peptide-to-lipid ratios (1:1000) and exhibits low micromolar activity against common Gram-positive and Gram-negative pathogenic bacteria. Remarkably, the four amino acids were sufficient to encode preferential poration of bacterial membranes with negligible damage to red blood cells at bactericidal concentrations. As the sequence is too short to span cellular membranes, pores are formed by stacking of channels in each bilayer leaflet.

Predicting drug delivery efficiency into tumor tissues through molecular simulation of transport in complex vascular networks.

Efficient delivery of anticancer drugs into tumor tissues at maximally effective and minimally toxic concentrations is vital for therapeutic success. At present, no method exists that can predict the spatial and temporal distribution of drugs into a target tissue after administration of a specific dose. This prevents accurate estimation of optimal dosage regimens for cancer therapy. Here we present a new method that predicts quantitatively the time-dependent spatial distribution of drugs in tumor tissues at sub-micrometer resolution. This is achieved by modeling the diffusive flow of individual drug molecules through the three-dimensional network of blood-vessels that vascularize the tumor, and into surrounding tissues, using molecular mechanics techniques. By evaluating delivery into tumors supplied by a series of blood-vessel networks with varying degrees of complexity, we show that the optimal dose depends critically on the precise vascular structure. Finally, we apply our method to calculate the optimal dosage of the cancer drug doxil into a section of a mouse ovarian tumor, and demonstrate the enhanced delivery of liposomally administered doxorubicin when compared to free doxorubicin. Comparison with experimental data and a multiple-compartment model show that the model accurately recapitulates known pharmacokinetics and drug-load predictions. In addition, it provides, for the first time, a detailed picture of the spatial dependence of drug uptake into tissues surrounding tumor vasculatures. This approach is fundamentally different to current continuum models, and reveals that the target tumor vascular topology is as important for therapeutic success as the transport properties of the drug delivery platform itself. This sets the stage for revisiting drug dosage calculations.

The importance of the membrane interface as the reference state for membrane protein stability.

The insertion of nascent polypeptide chains into lipid bilayer membranes and the stability of membrane proteins crucially depend on the equilibrium partitioning of polypeptides. For this, the transfer of full sequences of amino-acid residues into the bilayer, rather than individual amino acids, must be understood. Earlier studies have revealed that the most likely reference state for partitioning very hydrophobic sequences is the membrane interface. We have used µs-scale simulations to calculate the interface-to-transmembrane partitioning free energies ΔGS→TM for two hydrophobic carrier sequences in order to estimate the insertion free energy for all 20 amino acid residues when bonded to the center of a partitioning hydrophobic peptide. Our results show that prior single-residue scales likely overestimate the partitioning free energies of polypeptides. The correlation of ΔGS→TM with experimental full-peptide translocon insertion data is high, suggesting an important role for the membrane interface in translocon-based insertion. The choice of carrier sequence greatly modulates the contribution of each single-residue mutation to the overall partitioning free energy. Our results demonstrate the importance of quantifying the observed full-peptide partitioning equilibrium, which is between membrane interface and transmembrane inserted, rather than combining individual water-to-membrane amino acid transfer free energies.

Role of the Interaction Motif in Maintaining the Open Gate of an Open Sodium Channel.

Voltage-gated sodium channels undergo transitions between open, closed, and inactivated states, enabling regulation of the translocation of sodium ions across membranes. A recently published crystal structure of the full-length prokaryotic NavMs crystal structure in the activated open conformation has revealed the presence of a novel motif consisting of an extensive network of salt bridges involving residues in the voltage sensor, S4-S5 linker, pore, and C-terminal domains. This motif has been proposed to be responsible for maintaining an open conformation that enables ion translocation through the channel. In this study, we have used long-time molecular dynamics calculations without artificial restraints to demonstrate that the interaction network of full-length NavMs indeed prevents a rapid collapse and closure of the gate, in marked difference to earlier studies of the pore-only construct in which the gate had to be restrained to remain open. Interestingly, a frequently discussed "hydrophobic gating" mechanism at nanoscopic level is also observed in our simulations, in which the discontinuous water wire close to the gate region leads to an energetic barrier for ion conduction. In addition, we demonstrate the effects of in silico mutations of several of the key residues in the motif on the open channel's stability and functioning, correlating them with existing functional studies on this channel and homologous disease-associated mutations in human sodium channels; we also examine the effects of truncating/removing the voltage sensor and C-terminal domains in maintaining an open gate.

Correction to: Computed Free Energies of Peptide Insertion into Bilayers are Independent of Computational Method.

The original version of the article unfortunately contained an error in NIH support grant number RO1-GM74639 in the Acknowledgements section. The correct grant number is RO1-GM74637. This has been corrected with this erratum.

Molecular Dynamics Simulations Are Redefining Our View of Peptides Interacting with Biological Membranes.

Ever since the first molecular mechanics computer simulations of biological molecules became possible, there has been the dream to study all complex biological phenomena in silico, simply bypassing the enormous experimental challenges and their associated costs. For this, two inherent requirements need to be met: First, the time scales achievable in simulations must reach up to the millisecond range and even longer. Second, the computational model must accurately reproduce what is measured experimentally. Despite some recent successes, the general consensus in the field to date has been that neither of these conditions have yet been met and that the dream will be realized, if at all, only in the distant future. In this Account, we show that this view is wrong; instead, we are actually in the middle of the in silico molecular dynamics (MD) revolution, which is reshaping how we think about protein function. The example explored in this Account is a recent advance in the field of membrane-active peptides (MAPs). MD simulations have succeeded in accurately capturing the process of peptide binding, folding, and partitioning into lipid bilayers as well as revealing how channels form spontaneously from polypeptide fragments and conduct ionic and other cargo across membranes, all at atomic resolution. These game-changing advances have been made possible by a combination of steadily advancing computational power, more efficient algorithms and techniques, clever accelerated sampling schemes, and thorough experimental verifications. The great advantage of MD is the spatial and temporal resolution, directly providing a molecular movie of a protein undergoing folding and cycling through a functional process. This is especially important for proteins with transitory functional states, such as pore-forming MAPs. Recent successes are demonstrated here for the large class of antimicrobial peptides (AMPs). These short peptides are an essential part of the nonadaptive immune system for many organisms, ubiquitous in nature, and of particular interest to the pharmaceutical industry in the age of rising bacterial resistance to conventional antibiotic treatments. Unlike integral membrane proteins, AMPs are sufficiently small to allow converged sampling with the unbiased high-temperature sampling methodology outlined here and are relatively easy to handle experimentally. At the same time, AMPs exhibit a wealth of complex and poorly understood interactions with lipid bilayers, which allow not only tuning and validation of the simulation methodology but also advancement of our knowledge of protein-lipid interactions at a fundamental level. Space constraints limit our discussion to AMPs, but the MD methodologies outlined here can be applied to all phenomena involving peptides in membranes, including cell-penetrating peptides, signaling peptides, viral channel forming peptides, and fusion peptides, as well as ab initio membrane protein folding and assembly. For these systems, the promise of MD simulations to predict the structure of channels and to provide complete-atomic-detail trajectories of the mechanistic processes underlying their biological functions appears to rapidly become a reality. The current challenge is to design joint experimental and computational benchmarks to verify and tune MD force fields. With this, MD will finally fulfill its promise to become an inexpensive, powerful, and easy-to-use tool providing atomic-detail insights to researchers as part of their investigations into membrane biophysics and beyond.

Statistically derived asymmetric membrane potentials from α-helical and ß-barrel membrane proteins.

Modeling membrane protein (MP) folding, insertion, association and their interactions with other proteins, lipids, and drugs requires accurate transfer free energies (TFEs). Various TFE scales have been derived to quantify the energy required or released to insert an amino acid or protein into the membrane. Experimental measurement of TFEs is challenging, and only few scales were extended to depth-dependent energetic profiles. Statistical approaches can be used to derive such potentials; however, this requires a sufficient number of MP structures. Furthermore, MPs are tightly coupled to bilayers that are heterogeneous in terms of lipid composition, asymmetry, and protein content between organisms and organelles. Here we derived asymmetric implicit membrane potentials from ß-barrel and α-helical MPs and use them to predict topology, depth and orientation of proteins in the membrane. Our data confirm the 'charge-outside' and 'positive-inside' rules for ß-barrels and α-helical proteins, respectively. We find that the ß-barrel profiles have greater asymmetry than the ones from α-helical proteins, as a result of the different membrane architecture of gram-negative bacterial outer membranes and the existence of lipopolysaccharide in the outer leaflet. Our data further suggest that pore-facing residues in ß-barrels have a larger contribution to membrane insertion and stability than previously suggested.

Computed Free Energies of Peptide Insertion into Bilayers are Independent of Computational Method.

We show that the free energy of inserting hydrophobic peptides into lipid bilayer membranes from surface-aligned to transmembrane inserted states can be reliably calculated using atomistic models. We use two entirely different computational methods: high temperature spontaneous peptide insertion calculations as well as umbrella sampling potential-of-mean-force (PMF) calculations, both yielding the same energetic profiles. The insertion free energies were calculated using two different protein and lipid force fields (OPLS protein/united-atom lipids and CHARMM36 protein/all-atom lipids) and found to be independent of the simulation parameters. In addition, the free energy of insertion is found to be independent of temperature for both force fields. However, we find major difference in the partitioning kinetics between OPLS and CHARMM36, likely due to the difference in roughness of the underlying free energy surfaces. Our results demonstrate not only a reliable method to calculate insertion free energies for peptides, but also represent a rare case where equilibrium simulations and PMF calculations can be directly compared.